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3. Aerosol source apportionment uncertainty linked to the choice of input chemical components

Author

European Commission, Ministerio de Ciencia y Tecnología (España), 0000-0003-1546-9154, 0000-0002-8821-1923, 0000-0002-7294-3556, 0000-0003-2667-5973, 0000-0002-7720-0233, 0000-0002-9876-6383, 0000-0003-3307-2548, 0000-0002-7727-8647, 0000-0001-8010-7024, 0000-0003-1224-6369, 0000-0002-5453-5495, 0000-0002-6549-9899, Amato, Fulvio, van Drooge, Barend L., Jaffrezo, J. L., Favez, O., Colombi, C., Cuccia, E., Reche, Cristina, Ippolito, F., Ridolfo, S., Lara, R., Uzu, G., Ngoc, T. V. D., Dominutti, P., Darfeuil, S., Albinet, A., Srivastava, D., Karanasiou, Angeliki, Lanzani, G., Alastuey, Andrés, Querol, Xavier, European Commission, Ministerio de Ciencia y Tecnología (España), 0000-0003-1546-9154, 0000-0002-8821-1923, 0000-0002-7294-3556, 0000-0003-2667-5973, 0000-0002-7720-0233, 0000-0002-9876-6383, 0000-0003-3307-2548, 0000-0002-7727-8647, 0000-0001-8010-7024, 0000-0003-1224-6369, 0000-0002-5453-5495, 0000-0002-6549-9899, Amato, Fulvio, van Drooge, Barend L., Jaffrezo, J. L., Favez, O., Colombi, C., Cuccia, E., Reche, Cristina, Ippolito, F., Ridolfo, S., Lara, R., Uzu, G., Ngoc, T. V. D., Dominutti, P., Darfeuil, S., Albinet, A., Srivastava, D., Karanasiou, Angeliki, Lanzani, G., Alastuey, Andrés, and Querol, Xavier

Abstract

For a Positive Matrix Factorization (PMF) aerosol source apportionment (SA) studies there is no standard procedure to select the most appropriate chemical components to be included in the input dataset for a given site typology, nor specific recommendations in this direction. However, these choices are crucial for the final SA outputs not only in terms of number of sources identified but also, and consequently, in the source contributions estimates. In fact, PMF tends to reproduce most of PM mass measured independently and introduced as a total variable in the input data, regardless of the percentage of PM mass which has been chemically characterized, so that the lack of some specific source tracers (e.g. levoglucosan) can potentially affect the results of the whole source apportionment study. The present study elaborates further on the same concept, evaluating quantitatively the impact of lacking specific sources' tracers on the whole source apportionment, both in terms of identified sources and source contributions. This work aims to provide first recommendations on the most suitable and critical components to be included in PMF analyses in order to reduce PMF output uncertainty as much as possible, and better represent the most commons PM sources observed in many sites in Western countries. To this aim, we performed three sensitivity analyses on three different datasets across EU, including extended sets of organic tracers, in order to cover different types of urban conditions (Mediterranean, Continental, and Alpine), source types, and PM fractions. Our findings reveal that the vehicle exhaust source resulted to be less sensitive to the choice of analytes, although source contributions estimates can deviate significantly up to 44 %. On the other hand, for the detection of the non-exhaust one is clearly necessary to analyze specific inorganic elements. The choice of not analysing non-polar organics likely causes the loss of separation of exhaust and non-exhaust fa

Published
2024

4. A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

Author

Belis, C.A., Karagulian, F., Amato, F., Almeida, M., Artaxo, P., Beddows, D.C.S., Bernardoni, V., Bove, M.C., Carbone, S., Cesari, D., Contini, D., Cuccia, E., Diapouli, E., Eleftheriadis, K., Favez, O., El Haddad, I., Harrison, R.M., Hellebust, S., Hovorka, J., Jang, E., Jorquera, H., Kammermeier, T., Karl, M., Lucarelli, F., Mooibroek, D., Nava, S., Nøjgaard, J.K., Paatero, P., Pandolfi, M., Perrone, M.G., Petit, J.E., Pietrodangelo, A., Pokorná, P., Prati, P., Prevot, A.S.H., Quass, U., Querol, X., Saraga, D., Sciare, J., Sfetsos, A., Valli, G., Vecchi, R., Vestenius, M., Yubero, E., and Hopke, P.K.

Published
2015
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10. Analisi TOT per la determinazione di OC ed EC: interconfronto fra due differenti protocolli di misura

Author

Bernardoni, V, Bove M., C, Chiari, M, Cuccia, E, Daresta B., E, De Gennaro, G, Fermo, P, Giannoni, M, Lucarelli, F, Massabò, D, Nava, S, Piazzalunga, A, Prati, P, Valli, G, Vecchi, R, Bove M. C, Chiari M, Cuccia E, Daresta B. E, De Gennaro G, Fermo P, Giannoni M, Vecchi, R., PIAZZALUNGA, ANDREA, Bernardoni, V, Bove M., C, Chiari, M, Cuccia, E, Daresta B., E, De Gennaro, G, Fermo, P, Giannoni, M, Lucarelli, F, Massabò, D, Nava, S, Piazzalunga, A, Prati, P, Valli, G, Vecchi, R, Bove M. C, Chiari M, Cuccia E, Daresta B. E, De Gennaro G, Fermo P, Giannoni M, Vecchi, R., and PIAZZALUNGA, ANDREA

Published
2012

12. Results of monitoring campaign from a cruise ship in the Western Mediterranean

Author

Bove, M. C., Calzolai, G., Cavalli, F., Chiari, M., Cuccia, E., Hjorth, J., Massabò, D., Piazzalunga, A., Prati, P., Schembari, C., Bove, M, Calzolai, G, Cavalli, F, Chiari, M, Cuccia, E, Hjorth, J, Massabò, D, Piazzalunga, A, Prati, P, and Schembari, C

Subjects
marine aerosol, particulate matter
Published
2012

15. Confronto fra differenti tecniche per la determinazione dei carbonati

Author

PIAZZALUNGA, ANDREA, Bernardoni, V, Cuccia, E, Fermo, P, Yubero Funes, E, Massabò, D, Molteni, U, Perrone, M. R, Prati, P, Prato, M, Valli, G, Vassura, I, Vecchi, R., Piazzalunga, A, Bernardoni, V, Cuccia, E, Fermo, P, Yubero Funes, E, Massabò, D, Molteni, U, Perrone, M, Prati, P, Prato, M, Valli, G, Vassura, I, and Vecchi, R

Subjects
FT-IR, analisi termo-ottica, carbonati, particolato carbonioso, Thermal optical trasmittance, carbonati
Published
2012

19. Quantificazione dei carbonati mediante spettroscopia infrarossa

Author

PIAZZALUNGA, ANDREA, Fermo, P, Brambilla, L, Cuccia, E, Massabò, D, Prati, P, Vecchi, R, Paganelli, C., Piazzalunga, A, Fermo, P, Brambilla, L, Cuccia, E, Massabò, D, Prati, P, Vecchi, R, and Paganelli, C

Subjects
carbonati, particolato atmosferico
Published
2010

21. A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

Author

Belis, C, Karagulian, F, Amato, F, Almeida, M, Artaxo, P, Beddows, D, Bernardoni, V, Bove, M, Carbone, S, Cesari, D, Contini, D, Cuccia, E, Diapouli, E, Eleftheriadis, K, Favez, O, El Haddad, I, Harrison, R, Hellebust, S, Hovorka, J, Jang, E, Jorquera, H, Kammermeier, T, Karl, M, Lucarelli, F, Mooibroek, D, Nava, S, Nøjgaard, J, Paatero, P, Pandolfi, M, Perrone, M, Petit, J, Pietrodangelo, A, Pokorná, P, Prati, P, Prevot, A, Quass, U, Querol, X, Saraga, D, Sciare, J, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Yubero, E, Hopke, P, Belis, CA, Beddows, D. C. S, Bove, MC, Harrison, RM, Nøjgaard, J. K, PERRONE, MARIA GRAZIA, Petit, JE, Prevot, ASH, Hopke, PK, Belis, C, Karagulian, F, Amato, F, Almeida, M, Artaxo, P, Beddows, D, Bernardoni, V, Bove, M, Carbone, S, Cesari, D, Contini, D, Cuccia, E, Diapouli, E, Eleftheriadis, K, Favez, O, El Haddad, I, Harrison, R, Hellebust, S, Hovorka, J, Jang, E, Jorquera, H, Kammermeier, T, Karl, M, Lucarelli, F, Mooibroek, D, Nava, S, Nøjgaard, J, Paatero, P, Pandolfi, M, Perrone, M, Petit, J, Pietrodangelo, A, Pokorná, P, Prati, P, Prevot, A, Quass, U, Querol, X, Saraga, D, Sciare, J, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Yubero, E, Hopke, P, Belis, CA, Beddows, D. C. S, Bove, MC, Harrison, RM, Nøjgaard, J. K, PERRONE, MARIA GRAZIA, Petit, JE, Prevot, ASH, and Hopke, PK

Abstract

The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises employing real-world and synthetic input datasets. To that end, the results obtained by different practitioners using ten different RMs were compared with a reference. In order to explain the differences in the performances and uncertainties of the different approaches, the apportioned mass, the number of sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all evaluated using the methodology described in Belis et al. (2015). In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47 different source apportionment model results met the 50% standard uncertainty quality objective established for the performance test. In addition, 68% of the SCE uncertainties reported in the results were coherent with the analytical uncertainties in the input data. The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those better quantified by the models while those with contributions to the PM mass close to 1% represented a challenge. The results of the assessment indicate that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management.

Published
2015

23. Intercomparison of source apportionment approaches within the APICE-MED project

Author

Liguori, F., Latella, A., Pillon, S., Elvini, E., Patti, S., Prati, Paolo, Bove, MARIA CHIARA, Brotto, Paolo, Cassola, Federico, Cuccia, E., Marchand, N., Detournay, A., Salameh, D., Armengaud, A., Piga, D., Pey, J., Perez, N., Querol, X., Poupkou, A., Melas, D., Bartzis, G. J., Saraga, D. E., Tolis, E. I., Filiou, K., Quaglia, T., Zannetti, M. T., Parra, M., and Ferrnández, P.

Published
2013

26. Il progetto MED-APICE

Author

Liguori, F., Latella, A., Pillon, S., Elvini, E., De Vettori, S., Susanetti, L., De Bortoli, A., Patti, S., Prati, P., Bove, C., Brotto, P., Cassola, F., Cuccia, E., Massabò, D., Marchand, N., Detournay, A., Salameh, D., Armengaud, A., Piga, D., Pey, J., Querol, N. Perez e. X., Poupkou, A., Melas, D., Bartzis, G. J., Filiou, K., Saraga, D., Tolis, E. I., Quaglia, T., Miotto, A., Campostrini, P., Gissi, Elena, Meggiolaro, M., Zannetti, M. T., Brescianini, C., Parra, M., Ferrnández, P., Perez, C., and Repa, E.

Published
2012

33. Size-resolved comprehensive characterization of airborne particulate matter

Author

Cuccia, E, Massabò, D, Ariola, V, Bove, M, Fermo, P, Piazzalunga, A, Prati, P, Bove, MC, PIAZZALUNGA, ANDREA, Prati, P., Cuccia, E, Massabò, D, Ariola, V, Bove, M, Fermo, P, Piazzalunga, A, Prati, P, Bove, MC, PIAZZALUNGA, ANDREA, and Prati, P.

Abstract

An update of a methodology to extract both the size-segregated source apportionment of atmospheric aerosol and the size distribution of each detected element or compound, is presented. The approach is based on the parallel use of standard low-volume samplers to collect particulate matter (PM) and of an Optical Particle Counter (OPC). The methodology was introduced and validated in previous works for the average size distribution of elemental components of PM: it has now been extended to PM compounds such as ions and carbonaceous aerosol (namely, organic end elemental carbon, OC and EC). Furthermore, the methodology has been now adopted in the frame of a field campaign, proving that it is able to provide not only an average size distribution but also information on the time evolution of the size distribution of specific PM species. Samples were collected in the urban area of Genoa (Italy) and their composition was measured by Energy Dispersive X-ray Fluorescence (ED-XRF), Ion Chromatography (IC) and Thermo-optical analysis. Positive Matrix Factorization (PMF) was applied to time series of daily concentration values in PM10 and PM2.5 (i.e. fraction of PM mass with aerodynamic diameter lower that 10 mm and 2.5 mm, respectively) to identify major PM sources, and both PM mass concentration and sizesegregated particle number concentration were apportioned. Seven and six sources were respectively resolved in PM10 and PM2.5, with processes related to secondary aerosol formation accounting for about 53% and 57% of PM levels in the two fractions. The new methodology is complementary to sizesegregated PM sampling, and it was actually tested against a 13-stage nanoMOUDI cascade impactor. Size distributions obtained with the new methodology and directly measured by the nanoMOUDI turned out to be in good agreement (R 2> 0.60) with discrepancies observed for SO2 4 only.

Published
2013

36. Confronto fra differenti tecniche per la determinazione dei carbonati

Author

Piazzalunga, A, Bernardoni, V, Cuccia, E, Fermo, P, Yubero Funes, E, Massabò, D, Molteni, U, Perrone, M, Prati, P, Prato, M, Valli, G, Vassura, I, Vecchi, R, PIAZZALUNGA, ANDREA, Perrone, M. R, Vecchi, R., Piazzalunga, A, Bernardoni, V, Cuccia, E, Fermo, P, Yubero Funes, E, Massabò, D, Molteni, U, Perrone, M, Prati, P, Prato, M, Valli, G, Vassura, I, Vecchi, R, PIAZZALUNGA, ANDREA, Perrone, M. R, and Vecchi, R.

Published
2012

37. Results of monitoring campaign from a cruise ship in the Western Mediterranean

Author

Bove, M, Calzolai, G, Cavalli, F, Chiari, M, Cuccia, E, Hjorth, J, Massabò, D, Piazzalunga, A, Prati, P, Schembari, C, Bove, M. C, Schembari, C., PIAZZALUNGA, ANDREA, Bove, M, Calzolai, G, Cavalli, F, Chiari, M, Cuccia, E, Hjorth, J, Massabò, D, Piazzalunga, A, Prati, P, Schembari, C, Bove, M. C, Schembari, C., and PIAZZALUNGA, ANDREA

Published
2012

38. European intercomparison for Receptor Models: Preliminary Results

Author

Karagulian, F, Belis, C, Amato, F, Beddows, D, Bernardoni, V, Carbone, S, Cesari, D, Cuccia, E, Contini, D, Favez, O, El Haddad, I, Harrison, R, Kammermeier, T, Karl, M, Lucarelli, F, Nava, S, Nojgaard, J, Pandolfi, M, Perrone, M, Petit, J, Pietrodangelo, A, Prati, P, Prevot, A, Quass, U, Querol, X, Saraga, D, Sciare, J, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Schuer, J, Turner, J, Paatero, P, Hopke, P, Belis, CA, Beddows, DCS, Harrison, RM, Nojgaard, JK, Petit, JE, Prevot, AH, Schuer, JJ, Turner, JR, Hopke, PK, PERRONE, MARIA GRAZIA, Karagulian, F, Belis, C, Amato, F, Beddows, D, Bernardoni, V, Carbone, S, Cesari, D, Cuccia, E, Contini, D, Favez, O, El Haddad, I, Harrison, R, Kammermeier, T, Karl, M, Lucarelli, F, Nava, S, Nojgaard, J, Pandolfi, M, Perrone, M, Petit, J, Pietrodangelo, A, Prati, P, Prevot, A, Quass, U, Querol, X, Saraga, D, Sciare, J, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Schuer, J, Turner, J, Paatero, P, Hopke, P, Belis, CA, Beddows, DCS, Harrison, RM, Nojgaard, JK, Petit, JE, Prevot, AH, Schuer, JJ, Turner, JR, Hopke, PK, and PERRONE, MARIA GRAZIA

Published
2012

39. Quantificazione dei carbonati mediante spettroscopia infrarossa

Author

Piazzalunga, A, Fermo, P, Brambilla, L, Cuccia, E, Massabò, D, Prati, P, Vecchi, R, Paganelli, C, PIAZZALUNGA, ANDREA, Paganelli, C., Piazzalunga, A, Fermo, P, Brambilla, L, Cuccia, E, Massabò, D, Prati, P, Vecchi, R, Paganelli, C, PIAZZALUNGA, ANDREA, and Paganelli, C.

Published
2010

44. An integrated PM2.5 source apportionment study: Positive Matrix Factorisation vs. the chemical transport model CAMx.

Author

Bove, M. C., Brotto, P., Cassola, F., Cuccia, E., Massabò, D., Mazzino, A., Piazzalunga, A., and Prati, P.

Subjects
*PARTICULATE matter, *ATMOSPHERIC chemistry, *ATMOSPHERIC models, *CHEMICAL speciation, *AIR quality
Abstract

Receptor and Chemical Transport Models are commonly used tools in source apportionment studies, even if different expertise is required. We describe an experiment using both approaches to apportion the PM2.5 (i.e., particulate matter with aerodynamic diameters below 2.5 µm) sources in the city of Genoa (Italy). A sampling campaign was carried out to collect PM2.5 samples daily for approximately six month during 2011 in three sites. The subsequent compositional analyses included the speciation of elements, major ions and both organic and elemental carbon; these data produced a large database for receptor modelling through Positive Matrix Factorisation (PMF). In the same period, a meteorological and air quality modelling system was implemented based on the mesoscale numerical weather prediction model WRF and the chemical transport model CAMx to obtain meteorological and pollutant concentrations up to a resolution of 1.1 km. The source apportionment was evaluated by CAMx over the same period that was used for the monitoring campaign using the Particulate Source Apportionment Technology tool. Even if the source categorisations were changed (i.e., groups of time-correlated compounds in PMF vs. activity categories in CAMx), the PM2.5 source apportionment by PMF and CAMx produced comparable results. The different information provided by the two approaches (e.g., real-world factor profile by PMF and apportionment of a secondary aerosol by CAMx) was used jointly to elucidate the composition and origin of PM2.5 and to develop a more general methodology. When studying the primary and secondary components of PM, the main anthropogenic sources in the area were road transportation, energy production/industry and maritime emissions, accounting for 40%-50%, 20%-30% and 10%-15%, of PM2.5, respectively. [ABSTRACT FROM AUTHOR]

Published
2014
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45. A multi-wavelength optical set-up for the characterization of carbonaceous particulate matter

Author

E. Cuccia, A. Brunengo, V. Bernardoni, Gianluigi Valli, Dario Massabò, Maria Chiara Bove, Andrea Piazzalunga, Roberta Vecchi, Paolo Prati, Massabò, D, Bernardoni, V, Bove, M, Brunengo, A, Cuccia, E, Piazzalunga, A, Prati, P, Valli, G, and Vecchi, R

Subjects
Fluid Flow and Transfer Processes, Atmospheric Science, Angstrom exponent, Environmental Engineering, Chemistry, Mechanical Engineering, Analytical chemistry, Light scattering, X-ray fluorescence, Photometer, Carbon black, Particulates, Aethalometer, Pollution, law.invention, Absorbance, Black carbon, law, Gravimetric analysis, Light absorption, Particulate matter
Abstract

We introduce a simple, fully automatic, multi-wavelength and non-destructive optical system, namely a Multi-Wavelength Absorbance Analyzer (MWAA), to measure off-line the light absorption in Particulate Matter (PM) collected on filters and hence to derive the black and brown carbon content in the PM. This gives the opportunity to measure in the same sample the concentration of PM mass by gravimetric analysis, black and brown carbon, elements by, for instance, X Ray Fluorescence, and finally ions by Ion Chromatography. Furthermore, the multi-wavelength analysis gives the possibility to apportion black and brown/organic carbon sources through the direct determination of the Angstrom exponent and a revised version of the so-called "the aethalometer method". Up to 16 samples can be analyzed in sequence and in an automatic and controlled way within a few hours. The filter absorbance measured by MWAA was successfully validated both against a MAAP and a polar photometer. Our MWAA was used in several field campaigns with PM2.5 (PM with aerodynamic diameter less than 2.5 μm) samples collected on quartz fiber filters, and the mass absorption cross section at λ=405, 635 and 850. nm was obtained measuring at the same time the EC in particulate matter samples by thermal-optical analysis using the EUSAAR_2 protocol. First results on the MWAA application to the analysis of size-segregated samples collected by multi-stage impactor are also shown. © 2013 Elsevier Ltd.

Published
2013

46. A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

Author

E. Cuccia, Jean-Eudes Petit, Marco Pandolfi, Franco Lucarelli, Xavier Querol, Matthias Karl, D. Mooibroek, Marcelo Pinho Almeida, Eduardo Yubero, Pentti Paatero, Jakob Klenø Nøjgaard, Olivier Favez, Silvia Nava, Daniele Contini, Konstantinos Eleftheriadis, Evangelia Diapouli, Gianluigi Valli, A. Pietrodangelo, Paulo Artaxo, V. Bernardoni, Claudio A. Belis, Maria Chiara Bove, André S. H. Prévôt, Paolo Prati, Federico Karagulian, Philip K. Hopke, Daniela Cesari, Jan Hovorka, I. El Haddad, Petra Pokorná, Fulvio Amato, David C. S. Beddows, Héctor Jorquera, Dikaia Saraga, Athanasios Sfetsos, Maria Grazia Perrone, Roy M. Harrison, Mika Vestenius, Eunhwa Jang, Samara Carbone, Ulrich Quass, Roberta Vecchi, T. Kammermeier, Jean Sciare, Stig Hellebust, JRC Institute for Environment and Sustainability (IES), European Commission - Joint Research Centre [Ispra] (JRC), Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Universidade Nova de Lisboa = NOVA University Lisbon (NOVA), Institute for Physics, National Centre for Atmospheric Science, University of Birmingham [Birmingham], Department of Physics, Università degli Studi di Milano = University of Milan (UNIMI), Departement of Experimental Medicine, Section of Human Physiology, Università degli studi di Genova = University of Genoa (UniGe), Istituto di Scienze dell'Atmosfera e del Clima (ISAC), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Dipartimento di Fisica, ICT Institute of Politecnico di Milano, Institute of Nuclear Technology & Radiation Protection, National Center for Scientific Research 'Demokritos' (NCSR), Institut de recherches sur la catalyse et l'environnement de Lyon (IRCELYON), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Chimie de l'environnement (LCE), Aix Marseille Université (AMU)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Tasmanian Institute of Agriculture (TIA), University of Tasmania [Hobart, Australia] (UTAS), Norwegian Institute for Air Research (NILU), Università degli Studi di Napoli 'Parthenope' = University of Naples (PARTHENOPE), INTA - Instituto Nacional de Tecnología Agropecuaria, Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), Environmental Research Laboratory, National Centre for Scientific Research Demokritos, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Chimie Atmosphérique Expérimentale (CAE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of Rochester Medical Center, Universita degli Studi di Milano & National Institute of Nuclear Physics, University of Genoa (UNIGE), Consiglio Nazionale delle Ricerche [Roma] (CNR), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Universita degli studi di Napoli 'Parthenope' [Napoli], Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Belis, C, Karagulian, F, Amato, F, Almeida, M, Artaxo, P, Beddows, D, Bernardoni, V, Bove, M, Carbone, S, Cesari, D, Contini, D, Cuccia, E, Diapouli, E, Eleftheriadis, K, Favez, O, El Haddad, I, Harrison, R, Hellebust, S, Hovorka, J, Jang, E, Jorquera, H, Kammermeier, T, Karl, M, Lucarelli, F, Mooibroek, D, Nava, S, Nøjgaard, J, Paatero, P, Pandolfi, M, Perrone, M, Petit, J, Pietrodangelo, A, Pokorná, P, Prati, P, Prevot, A, Quass, U, Querol, X, Saraga, D, Sciare, J, Sfetsos, A, Valli, G, Vecchi, R, Vestenius, M, Yubero, E, and Hopke, P

Subjects
Atmospheric Science, Source apportionment, 010504 meteorology & atmospheric sciences, Meteorology, AIR-QUALITY, Intercomparison exercise, Source apportionment Receptor models Intercomparison exercise Model performance indicators Model uncertainty Particulate matter, 010501 environmental sciences, 114 Physical sciences, 01 natural sciences, complex mixtures, Receptor models, POLLUTION, AEROSOLS, Time windows, Apportionment, Environmental Science(all), Statistics, Air quality management, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Air quality index, 1172 Environmental sciences, 0105 earth and related environmental sciences, General Environmental Science, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, PM SOURCE APPORTIONMENT, Time trends, AREA, respiratory system, Objective quality, Model performance indicator, CHIM/12 - CHIMICA DELL'AMBIENTE E DEI BENI CULTURALI, 13. Climate action, Model uncertainty, Model performance indicators, Environmental science, Receptor model, Standard uncertainty, Particulate matter
Abstract

The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises employing real-world and synthetic input datasets. To that end, the results obtained by different practitioners using ten different RMs were compared with a reference. In order to explain the differences in the performances and uncertainties of the different approaches, the apportioned mass, the number of sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all evaluated using the methodology described in Bells et al. (2015). In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47 different source apportionment model results met the 50% standard uncertainty quality objective established for the performance test. In addition, 68% of the SCE uncertainties reported in the results were coherent with the analytical uncertainties in the input data. The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those better quantified by the models while those with contributions to the PM mass close to 1% represented a challenge. The results of the assessment indicate that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management. (C) 2015 The Authors. Published by Elsevier Ltd.

Published
2015

47. Carbonate measurements in PM10 near the marble quarries of Carrara (Italy) by infrared spectroscopy (FT-IR) and source apportionment by positive matrix factorization (PMF)

Author

Ugo Molteni, Andrea Piazzalunga, V. Bernardoni, Paolo Prati, Dario Massabò, Roberta Vecchi, Laura Brambilla, Gianluigi Valli, Paola Fermo, E. Cuccia, Cuccia, E, Piazzalunga, A, Bernardoni, V, Brambilla, L, Fermo, P, Massabo, D, Molteni, U, Prati, P, Valli, G, and Vecchi, R

Subjects
Pollution, Atmospheric Science, carbonate, particulate matter, FT-IR, media_common.quotation_subject, Mineralogy, Infrared spectroscopy, Average level, Particulates, chemistry.chemical_compound, chemistry, Carbonate, Environmental science, Fourier transform infrared spectroscopy, General Environmental Science, media_common
Abstract

The concentration of carbonates in atmospheric Particulate Matter (PM) is usually quite low. The surroundings of marble quarries are peculiar sites where the impact of carbonates in PM levels can be significant. We present here the results of a PM10 sampling campaign performed in Carrara (Italy). The town lies between the famous marble quarries and the harbour: about 1000 trucks per day transport marble blocks and debris from the quarries to the harbour passing through the town centre. PM10 was collected on daily basis on PTFE filters analyzed by Energy-Dispersive X-Ray Fluorescence (ED-XRF) and Ion-Chromatography (IC). Carbonate concentration was measured by a non-destructive Infrared Spectroscopy analysis (FT-IR). Time series of elemental (Na–Pb by ED-XRF), ionic (SO42−, NH4+ by ion-chromatography) and carbonate (by FT-IR) concentration values were merged in a unique data set and a PMF analysis singled out the major PM10 sources in the area. Marble transportation turned out to be the major pollution source in the town accounting to PM10 for about 36%; this corresponded to a CaCO3 average level of about 8 μg m−3 during working days. The FT-IR analysis was a crucial part of the work and an ad-hoc analytical procedure was specifically set up, calibrated, and tested as described in the text.

Published
2011

48. Size-resolved comprehensive characterization of airborne particulate matter

Author

Maria Chiara Bove, E. Cuccia, Andrea Piazzalunga, Paolo Prati, Dario Massabò, Paola Fermo, V. Ariola, Cuccia, E, Massabò, D, Ariola, V, Bove, M, Fermo, P, Piazzalunga, A, and Prati, P

Subjects
Atmospheric Science, Optical counter, Particle number, Chemistry, Ion chromatography, Analytical chemistry, Sampling (statistics), Fraction (chemistry), Size distribution, Particulates, Aerosol, Positive matrix factorization, Mass concentration (chemistry), Particulate matter, Particle counter, General Environmental Science
Abstract

An update of a methodology to extract both the size-segregated source apportionment of atmospheric aerosol and the size distribution of each detected element or compound, is presented. The approach is based on the parallel use of standard low-volume samplers to collect particulate matter (PM) and of an Optical Particle Counter (OPC). The methodology was introduced and validated in previous works for the average size distribution of elemental components of PM: it has now been extended to PM compounds such as ions and carbonaceous aerosol (namely, organic end elemental carbon, OC and EC). Furthermore, the methodology has been now adopted in the frame of a field campaign, proving that it is able to provide not only an average size distribution but also information on the time evolution of the size distribution of specific PM species. Samples were collected in the urban area of Genoa (Italy) and their composition was measured by Energy Dispersive X-ray Fluorescence (ED-XRF), Ion Chromatography (IC) and Thermo-optical analysis. Positive Matrix Factorization (PMF) was applied to time series of daily concentration values in PM10 and PM2.5 (i.e. fraction of PM mass with aerodynamic diameter lower that 10 μm and 2.5 μm, respectively) to identify major PM sources, and both PM mass concentration and size-segregated particle number concentration were apportioned. Seven and six sources were respectively resolved in PM10 and PM2.5, with processes related to secondary aerosol formation accounting for about 53% and 57% of PM levels in the two fractions. The new methodology is complementary to size-segregated PM sampling, and it was actually tested against a 13-stage nanoMOUDI cascade impactor. Size distributions obtained with the new methodology and directly measured by the nanoMOUDI turned out to be in good agreement ( R 2 > 0.60) with discrepancies observed for SO 4 2 − only.

Published
2012

49. Multiple sclerosis in twins from continental Italy and Sardinia: a nationwide study

Author

Ristori G, Cannoni S, Stazi MA, Vanacore N, Cotichini R, Alfo M, Pugliatti M, Sotgiu S, Solaro C, Bomprezzi R, Di Giovanni S, Figa` Talamanca L, Nistico L, Fagnani C, Neale MC, Cascino I, Giorgi G, Battaglia MA, Buttinelli C, Tosi R, Salvetti M, M. Melato, R. Dellantonio, L. Sironi, E. Bottacchi, M. Signorino, R. Angeloni, L. Curatola, C. Paci, M. Ragno, G. Sirocchi, AM. Vurchio, E. Duc, D. Spitaleri, M. Trojano, M. Liguori, N. Cimini, G. Moretto, M. Porta, MR Rottoli A. Mamoli, M. Camerlingo, E. Nardozza, T. Sacquegna, S. Stecchi, C. Scandellari, L. Callea, R. Capra, M. Codella, M. G. Marrosu, E. Cocco, A. Spissu, G. Cossu, S. Tronci, A. Di Lauro, E. Lombardi, A. Reggio, F. Patti, P. Valentino, A. Quattrone, D Farina, M. E. Nives, A. Lugaresi, F. Perla, M. G. Rosso, M. R. Tola, E. Granieri, MP Amato L. Massacesi, E. Millefiorini, V. Durastanti, G. L. Mancardi, A. Murialdo, NR. Pizio, P. Bellantonio, R. Fantozzi, R. Totaro, A. Carolei, F. Giramma, A. T. Lazzaro, C. Giraldi, M. Mazzoni, G. Giuliani, E. Pucci, P. Previdi, MC. Fazio, M. Buccafusca, P. Girlanda, C. Messina, G. D’Aleo, C. Milanese, L. Lamantia, D. Caputo, E. Scarpini, R. Clerici, L. Moiola, M. Gironi, E. Merelli, F. Casoni, S. Bonavita, G. Tedeschi, M. Leone, D. Mittino, SB. Murgia, L. Musu, P. Gallo, P. Perini, E. Frasson, G. Salemi, G. Cuccia, E. Montanari, L. Manneschi, D. Saviola, M. Antonelli, V. Cosi, R. Bergamaschi, V. Gallai, D. Murasecco, P. Sarchielli, R. Urcioli, G. Perticoni, G. Meucci, G. Moscato, B. Lucci, E. Covezzi, MG. Coniglio, D. Acquistapace, L. Motti, B. Dossi Curro`, M. Frontoni, C. Mainero, P. Giannetti, I. Pestalozza, S. Di Legge, M. Spadaro, C. Pozzilli, S. Romano, B. Mercuri, C. Scoppetta, C. Gasperini, S. Galgani, MG. Grasso, S. Paolucci, PA. Tonali, A. Leonardi, A. Oneto, G Rosati, M. A. Sotgiu, A. Bertolotto, M. Capobianco, L. Durelli, M. Clerico, L. Sosso, R. Bongioanni, D. Orrico, C. Carbonin, U. Freo. M. Zaffaroni, A. Ghezzi, N. Falcone, Ristori G, Cannoni S, Stazi MA, Vanacore N, Cotichini R, Alfo M, Pugliatti M, Sotgiu S, Solaro C, Bomprezzi R, Di Giovanni S, Figa` Talamanca L, Nistico L, Fagnani C, Neale MC, Cascino I, Giorgi G, Battaglia MA, Buttinelli C, Tosi R, Salvetti M, M. Melato, R. Dellantonio, L.Sironi, E. Bottacchi, M. Signorino, R. Angeloni, L. Curatola, C. Paci, M. Ragno, G. Sirocchi, AM. Vurchio, E. Duc, D. Spitaleri, M. Trojano, M. Liguori, N. Cimini, G. Moretto, M. Porta, MR Rottoli A. Mamoli, M. Camerlingo, E. Nardozza, T. Sacquegna, S. Stecchi, C. Scandellari, L. Callea, R. Capra, M. Codella, M. G. Marrosu, E. Cocco, A. Spissu, G. Cossu, S. Tronci, A. Di Lauro, E. Lombardi, A. Reggio, F. Patti, P. Valentino, A. Quattrone, D Farina, M. E. Nives, A. Lugaresi, F. Perla, M. G. Rosso, M.R. Tola, E. Granieri, MP Amato L. Massacesi, E. Millefiorini, V. Durastanti, G. L. Mancardi, A. Murialdo, NR. Pizio, P. Bellantonio, R. Fantozzi, R. Totaro, A. Carolei, F. Giramma, A. T. Lazzaro, C. Giraldi, M. Mazzoni, G. Giuliani, E. Pucci, P. Previdi, MC. Fazio, M. Buccafusca, P. Girlanda, C. Messina, G. D’Aleo, C. Milanese, L. Lamantia, D. Caputo, E. Scarpini, R. Clerici, L. Moiola, M. Gironi, E. Merelli, F. Casoni, S. Bonavita, G. Tedeschi, M. Leone, D. Mittino, SB. Murgia, L. Musu, P. Gallo, P. Perini, E. Frasson, G. Salemi, G. Cuccia, E. Montanari, L. Manneschi, D. Saviola, M. Antonelli, V. Cosi, R. Bergamaschi, V. Gallai, D. Murasecco, P. Sarchielli, R. Urcioli, G. Perticoni, G. Meucci, G. Moscato, B. Lucci, E. Covezzi, MG. Coniglio, D. Acquistapace, L. Motti, B. Dossi Curro`, M. Frontoni, C. Mainero, P. Giannetti, I. Pestalozza, S. Di Legge, M. Spadaro, C. Pozzilli, S. Romano, B. Mercuri, C. Scoppetta, C. Gasperini, S. Galgani, MG. Grasso, S. Paolucci, PA. Tonali, A. Leonardi, A. Oneto, and G Rosati, M. A. Sotgiu, A. Bertolotto, M. Capobianco, L. Durelli, M. Clerico, L. Sosso, R. Bongioanni, D. Orrico, C. Carbonin, U. Freo. M. Zaffaroni, A. Ghezzi, N. Falcone

Subjects
Male, Questionnaires, Multiple Sclerosis, Concordance, Population, Twins, Dizygotic twins, Disease cause, Multiple Sclerosis, Epidemiology, Twins, Cohort Studies, Cohort Studies, Disease Susceptibility, Female, Genetic Predisposition to Disease, Humans, Italy, epidemiology, Male, Multiple Sclerosis, epidemiology/genetics, Questionnaires, Regression Analysis, Twins, Surveys and Questionnaires, medicine, Humans, Genetic Predisposition to Disease, education, education.field_of_study, epidemiology/genetics, business.industry, Multiple sclerosis, medicine.disease, Penetrance, Twin study, Confidence interval, Neurology, Italy, Regression Analysis, Settore MED/26 - Neurologia, Female, epidemiology, Neurology (clinical), Disease Susceptibility, business, Demography
Abstract

Knowledge about the balance between heritable and nonheritable risk in multiple sclerosis (MS) is based on twin studies in high-prevalence areas. In a study that avoided ascertainment limitations and directly compared continental Italy (medium-prevalence) and Sardinia (high-prevalence), we ascertained 216 pairs from 34,549 patients. This gives a twinning rate of 0.62% among MS patients, significantly less than that of the general population. In continental Italy, probandwise concordance was 14.5% (95% confidence interval, 5.1-23.8) for monozygotic and 4.0% (95% confidence interval, 0.8-7.1) for dizygotic twins. Results in Sardinia resemble those in northern populations but in limited numbers. Monozygotic concordance was 22.2% (95% confidence interval, 0-49.3) probandwise, but no concordant dizygotic pairs were identified. A questionnaire on 80 items possibly related to disease cause was administered to 70 twin pairs, 135 sporadic patients, and 135 healthy volunteers. Variables positively (7) or negatively (2) associated with predisposition and concordance in twins largely overlapped and were mainly linked to infection. If compared with previous studies, our data demonstrate that penetrance in twins appears to correlate with MS prevalence. They highlight the relevance of nonheritable variables in Mediterranean areas. The apparent underrepresentation of MS among Italian twins draws attention to protective factors, shared by twins, that may influence susceptibility.

Published
2006

50. A PM10 chemically characterized nation-wide dataset for Italy. Geographical influence on urban air pollution and source apportionment.

Author

Pietrodangelo A, Bove MC, Forello AC, Crova F, Bigi A, Brattich E, Riccio A, Becagli S, Bertinetti S, Calzolai G, Canepari S, Cappelletti D, Catrambone M, Cesari D, Colombi C, Contini D, Cuccia E, De Gennaro G, Genga A, Ielpo P, Lucarelli F, Malandrino M, Masiol M, Massabò D, Perrino C, Prati P, Siciliano T, Tositti L, Venturini E, and Vecchi R

Abstract

Urban textures of the Italian cities are peculiarly shaped by the local geography generating similarities among cities placed in different regions but comparable topographical districts. This suggested the following scientific question: can different topographies generate significant differences on the PM 10 chemical composition at Italian urban sites that share similar geography despite being in different regions? To investigate whether such communalities can be found and are applicable at Country-scale, we propose here a novel methodological approach. A dataset comprising season-averages of PM 10 mass concentration and chemical composition data was built, covering the decade 2005-2016 and referring to urban sites only (21 cities). Statistical analyses, estimation of missing data, identification of latent clusters and source apportionment modeling by Positive Matrix Factorization (PMF) were performed on this unique dataset. The first original result is the demonstration that a dataset with atypical time resolution can be successfully exploited as an input matrix for PMF obtaining Country-scale representative chemical profiles, whose physical consistency has been assessed by different tests of modeling performance. Secondly, this dataset can be considered a reference repository of season averages of chemical species over the Italian territory and the chemical profiles obtained by PMF for urban Italian agglomerations could contribute to emission repositories. These findings indicate that our approach is powerful, and it could be further employed with datasets typically available in the air pollution monitoring networks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Published
2024
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